We propose a digital twin-assisted adaptive preloading scheme to enhance bandwidth efficiency and user quality of experience (QoE) in short video streaming. We first analyze the relationship between the achievable throughput and video bitrate and critical factors that affect the preloading decision, including the buffer size and bitrate selection. We then construct a digital twin-assisted adaptive preloading framework for short video streaming. By collecting and analyzing historical throughput and tracking behavior information, a throughput prediction model and a probabilistic model can be constructed to accurately predict future throughput and user behavior, respectively. Using the predicted information and real-time running status data from a short video application, we design a preloading strategy to enhance bandwidth efficiency while guaranteeing user QoE. Simulation results demonstrate the effectiveness of our proposed scheme comparing with the state-of-the-art preloading schemes.

翻译：本文提出了一种数字孪生辅助的自适应预加载方案，以提升短视频流中的带宽效率与用户体验质量（QoE）。我们首先分析了可达吞吐量与视频比特率之间的关系，以及影响预加载决策的关键因素，包括缓冲区大小和比特率选择。随后，我们构建了一个数字孪生辅助的短视频流自适应预加载框架。通过收集和分析历史吞吐量及用户浏览行为信息，分别构建吞吐量预测模型与概率模型，以准确预测未来吞吐量与用户行为。基于预测信息及短视频应用的实时运行状态数据，我们设计了一种预加载策略，在保障用户QoE的同时增强带宽效率。仿真结果表明，与现有最先进的预加载方案相比，所提方案具有有效性。